Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14470
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorRamesh, H.-
dc.contributor.authorC. A, Rishikeshan-
dc.date.accessioned2020-08-27T10:02:56Z-
dc.date.available2020-08-27T10:02:56Z-
dc.date.issued2019-
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14470-
dc.description.abstractThe thesis evolves on the development of novel feature extraction methods for the analysis of remotely sensed images which are enabled to enhance the robustness and the generalization properties of the feature extraction system. Recent developments in optical data sensors mounted on-board of both space-borne and airborne earth observation platforms have led to increasing volume, acquisition speed and a variety of sensed images. Therefore the feature extraction from remotely sensed imageries is a major concern and challenge for the photogrammetry, remote sensing, and GIS communities. The extensive survey of literatures expose the shortcomings overlooked for the existing approaches utilized in the feature extraction of remote sensing images. The automated extraction of features from the remotely sensed images has been an active area of research for over a decade due to its substantial role in several application areas viz. urban planning, transportation navigation, traffic management, emergency handling, etc. Although the concept of feature extraction is relatively simple, the reliability and accuracy remains a major challenge. With advanced imaging technologies, there is an augmented demand for developing new approaches which can exhaustively explore the information embedded in remote sensing images. The past studies evidenced mathematical morphological tools as best suited for the potential exploitation of the spatial information in the remote sensing imageries. Priorly, mathematical morphology was applied only for the interpretation of binary images. However, it was extended to analyze grey scale and colour images. The thesis presents different spatial feature extraction methods which are developed based on mathematical morphology for the analysis of remote sensing optical images addressing to different applications such as urban feature detection, waterbody extraction, crop field boundary extraction and shoreline extraction. The morphology based feature extraction algorithms developed are effective and contribute to the interpretation of high resolution remotely sensed images.This automatic, scalable, and parallel processing methods can be used to analyze colossal remote sensing data within the selected classification schemes of remote sensing image system. The proposed methodologies contribute to the operational use of remote sensing datasets in manyii practical applications related to monitoring and management of environmental resources. In this thesis, a novel approach is presented for extracting shoreline from remotely sensed images. Shoreline extraction is inevitable for several studies such as coastal zone management, coastline erosion monitoring, GIS database updating, watershed definition, flood plain mapping and the evaluation of water resources. Multiple techniques are proposed for the extraction of different types of waterbodies such as lakes, rivers and glacier lakes. MM techniques have been exploited for the extraction of crop field boundaries from multiple satellite imageries. UAV driven images are beneficial as they facilitate a comprehensive description of the scenes, and concurrently require pertinent image processing techniques to exploit the geometrical information from the image datasets. This study introduces two innovative feature extraction methods for UAV and satellite images The novel feature extraction techniques proposed in the thesis have been investigated and experimented in different datasets to test their degree of performance. The experimental investigation performed with the developed techniques for analysis of remotely sensed images are noted for its improved accuracy when compared against other state of the art methods.en_US
dc.language.isoenen_US
dc.publisherNational Institute of Technology Karnataka, Surathkalen_US
dc.subjectDepartment of Applied Mechanics and Hydraulicsen_US
dc.subjectRemote sensingen_US
dc.subjectimage processingen_US
dc.subjectcomputer visionen_US
dc.subjectmathematical morphologyen_US
dc.subjectclassificationen_US
dc.subjectshoreline detectionen_US
dc.subjectwaterbody extractionen_US
dc.subjectcrop field boundary delineationen_US
dc.subjectbuilding extractionen_US
dc.subjectUAVen_US
dc.subjectVHR imagesen_US
dc.subjecturban featuresen_US
dc.titleFeature Extraction Strategies based on Mathematical Morphology for the Analysis of Remotely Sensed Imageryen_US
dc.typeThesisen_US
Appears in Collections:1. Ph.D Theses

Files in This Item:
File Description SizeFormat 
148037AM14F06.pdf7.64 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.